{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Matplotlib可视化的学习与实践**\n",
    "\n",
    "简介：Python可视化库，Python数据分析库的“三驾马车”之一。\n",
    "\n",
    "[官网](https://matplotlib.org/)：\n",
    "> Matplotlib tries to make easy things easy and hard things possible.\n",
    "\n",
    "本文纲要：\n",
    "- Matplotlib绘图框架\n",
    "- 基础图表绘制\n",
    "- 图表元素调校\n",
    "- 组合图\n",
    "- 三维及科学可视化\n",
    "- 动态可视化\n",
    "- 底图水印\n",
    "- 主题色配置\n",
    "- 其他深入话题简介\n",
    "\n",
    "## Matplotlib绘图框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T04:10:32.882031Z",
     "start_time": "2020-03-26T04:10:29.402464Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt #导入对应的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T04:10:32.894970Z",
     "start_time": "2020-03-26T04:10:32.884998Z"
    }
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'x':['Mon.','Tue.','Wed.','Thu.','Fri.'],\n",
    "                 'y':[76,37,90,60,50],\n",
    "                 'z':[37,46,53,81,60]})  #示例数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T04:10:34.370150Z",
     "start_time": "2020-03-26T04:10:34.168312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8f83b230b8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在jupyter notebook环境下\n",
    "%matplotlib inline \n",
    "#将图直接在jupyter notebook环境下输出，不需再写 plt.show()\n",
    "fig, ax = plt.subplots() #fig = plt.figure() ax= fig.add_subplot(111)\n",
    "ax.plot(df['x'],df['y'],'o') #散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T09:22:41.218947Z",
     "start_time": "2020-03-24T09:22:41.111232Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在shell 脚本环境下：\n",
    "fig, ax = plt.subplots() \n",
    "ax.plot(df['x'],df['y'],'o') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两套可视化接口，plt.plot()系列和 fig, ax = plt.subplots();ax.plot() 系列，分别对应MATLAB的陈述式语法和面向对象写法；\n",
    "具体可参考[lifecycle.html](https://matplotlib.org/tutorials/introductory/lifecycle.html#sphx-glr-tutorials-introductory-lifecycle-py)\n",
    "plt.plot()适合于快速出图，ax.plot()更方便用于精细绘图；\n",
    "\n",
    "plt.plot(df['x'],df['y'],'o') \n",
    "\n",
    "等价于\n",
    "\n",
    "fig, ax = plt.subplots() \n",
    "ax.plot(df['x'],df['y'],'o') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T09:41:31.562885Z",
     "start_time": "2020-03-24T09:41:31.420290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21a565e39b0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['x'],df['y'],'o')   #ax.plot(df['x'],df['y'],'o') 的效果上面已展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基础图表绘制\n",
    "画散点图、折线图、柱状图、条形图、饼图、直方图、箱线图\n",
    "\n",
    "### 散点图\n",
    "\n",
    "**散点图**两种主要方法：\n",
    "- scatter(x,y)\n",
    "- plot(x,y,'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T04:10:39.297376Z",
     "start_time": "2020-03-26T04:10:39.293387Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "#配置一套主题色\n",
    "mpl.rcParams[\"axes.prop_cycle\"]=mpl.cycler('color', ['1EAFAE', 'A3FFFF', '69FFFF', 'BA5C25', 'FFA069', '9E5B3A', 'D7CE88'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T09:48:34.268125Z",
     "start_time": "2020-03-24T09:48:34.141466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots() \n",
    "ax.scatter(df['x'],df['y'])\n",
    "ax.set_ylim(0,100) #设置y轴范围为[0,100]，同理设置x周用 .set_xlim(0,100)\n",
    "#另外的写法  axes.set(ylim=[0,100])  plt.ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T05:36:19.002558Z",
     "start_time": "2020-03-26T05:36:18.791095Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scatter(x,y) 的参数\n",
    "\n",
    "#### 气泡图\n",
    "\n",
    "把颜色和半径属性也用上，因为color不能赋值分类变量，因此如果要用颜色区分类型需先做筛选（或者用形状做类型区分<也还是需要做筛选>）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T08:02:34.256358Z",
     "start_time": "2020-03-25T08:02:34.137712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点大小参数，画气泡图 ax.plot(df['x'],df['y'],markersize=df['z']) 不可以,markersize接受的是float不是series\n",
    "fig, ax = plt.subplots() \n",
    "ax.scatter(df['x'],df['y'], s=df['z'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8f74cd70f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAlxEZBSOJRLcvXkTa/ftI+VOIrhF98Cg+6jv7u7mey0tfK+lhVvnz+eL556Xk165xsBFRLK0+9gxPvLz9Ty5bx99qdTJ8B5KXypFXyrFP+7axcee3kBHX9+o21eAi4hk4e2eHq5/7lk6+vroTaUyXu94Msme7m4+9ewzHEkkRlWDAlxEZITcnTuaX+JIIkHm0f07A+4c6O3lK6+9Oqo6FOAiIiP0r/v2sb2ri+QoHknZl0qxrq2NFzs6st6GAlxEZIS+/cbrwz7sORO9yST37diR9foZnYViZruBo0ASGHD3JjOrAR4GGoDdwI3ufjjrSkREQmDn0aO0HT+ek2058Kv2gxxLJKgaN27E64+kB/4Rd1866MnIdwIb3L0R2BBMi4i8p712+HBOhy7i0Shbu7qyWnc0dVwHrAnerwGuH8W2RERC4Y0jR+jOwfDJCQPu7Dx6NKt1Mw1wB9aZ2UYzWxXMq3P3tuD9fqBuqBXNbJWZNZtZc3t7e1ZFiogUi+PJgZxuL+lOXyq7/xAyvRLzUndvNbNpwHoze33wh+7uZjbk4Vh3Xw2sBmhqasr+kK2ISBGoLisjAlmdPjiUmBkTYiMf/4YMe+Du3hq8HgQeBy4CDpjZDIDg9WBWFYiIhMjiSdWMj+XuLiQRM86bNCm7dYdbwMwqzWzCiffAR4EtwFpgZbDYSuCJrCoQEQmR99fUkBjBlZfDSbqzcOLErNbN5L+ROuBxMzux/APu/pSZvQQ8Yma3AXuAG7OqQEQkRKbF4yyfMoVf5uCYXsyMT8+Zc/LOhSNef7gF3H0XsGSI+YeAK7NqVUQkxP783PNoPvSLEd0DZSixSIQ/XtCY9fq6ElNEZIQurKnhhrlziY/ilrAV0ShfOvdc5lRWZr0NBbiISBa+uvh8zquuzirEK6JRrqibzm3zF4yqBgW4iEgW4tEoD15yKZdPq6MiGh3RejfMnsN3li8nkj62mDU9kUdEJEvxaJS//8AH+ElrK3dteo2eZJLugdMv9DHSve7aeJx7l76fD9bW5qR9BbiIyChdO3MmH6+v55ftB1nf1sZLhw7RHjzUuL6igoumTuVjM+pZVlODjbLXPZgCXEQkByJmXD6tjsunDXlXkfy0WbCWREQkpxTgIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJKQW4iEhIKcBFREJKAS4iElIKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSGQe4mUXN7BUz+3EwPc/MXjCznWb2sJmV5a9MERE51Uh64H8KbB80fS/wTXdfABwGbstlYSIicnYZBbiZzQKuBX4QTBtwBfBosMga4Pp8FCgiIkPLtAf+LeC/AqlgegrQ6e4DwfQ+YGaOaxMRkbMYNsDN7BPAQXffmE0DZrbKzJrNrLm9vT2bTYiIyBAy6YFfAnzSzHYDD5EeOvkboNrMYsEys4DWoVZ299Xu3uTuTbW1tTkoWUREAGLDLeDuXwa+DGBmK4AvuftnzOxHwA2kQ30l8EQe65QMdfT18fI7hziaGCAejbJgwgQWTZw41mWJSB4MG+Bn8RfAQ2b2V8ArwP25KUmy0XzoEN9vaeHZgwcYF4mQcidiRtKd2ePHc/vChVxbP5PyaHSsSxWRHDF3L1hjTU1N3tzcXLD2SkHKnf+5eTMP7tlNbzLJmX6a46NR5lRW8sAllzKlvLygNYrI6JjZRndvOnW+rsQMubs2beLBPbs5fpbwBuhJJnnz6FE+9dyzHE0kClafiOSPAjzEnt6/nx/t3cPxZDKj5RPutB0/zldfey3PlYlIISjAQ+xvd7yRcXif0J9K8dO3W+ns789TVSJSKArwkNp97BhbOjuzWjdixsN79uS4IhEpNAV4SD1z8ACW5brHk0mebN2X03pEpPAU4CHV1Z+gL5UafsEzrq8hFJGwU4CHVFkkQsSy7YOn1xeRcNO/4pCaXTme+CguyplbWZXDakRkLCjAQ+qq6TPI9iKsyliMW+fPz3FFIlJoCvCQikej3Dy3gXFZDKNUxWJ8SDcWEwk9BXiIrWpsHPEwSjwa5SuLF49q/FxEioMCPMRmVFTwT5dcwvhoNKNTCiuiUe5YuJBPzpqd99pEJP8U4CG3dHINT3x4BfOqqqiIRof8gVZGY0yIxfgfFyzh84veV/AaRSQ/RnM7WSkSCydO5Okrr+K1zsOsbmnh+Y4Ojg8MUBaNMreykj9e0Mg19fU6dVDkPUYB/h5hZiydXMN3L7p4rEsRkQJRl0xEJKQK+kAHM2sHRnsXpalARw7KKQXaV5nTvsqc9lXmcrWv5rr7aef+FjTAc8HMmod6MoWcTvsqc9pXmdO+yly+95WGUEREQkoBLiISUmEM8NVjXUCIaF9lTvsqc9pXmcvrvgrdGLiIiKSFsQcuIiIowEVEQmvMA9zM3Mz+adB0zMzazezHY1lXsTKzKWb2avBnv5m1DpouG+v6ioGZfdPMvjBo+mdm9oNB098wsy9muK27zexL+aizmJzle9VpZtvGur5iZmbJQfvuVTNrGGKZejN7NNdtF8Ol9N3AYjOrcPfjwNVA6xjXVLTc/RCwFNLhAhxz96+PaVHF51fAjcC3zCxC+mKKiYM+/xDwZ2NRWLE60/cqCCN1ps7uuLsvPdOHZhZz97eBG3Ld8Jj3wAM/Ba4N3t8CPHjiAzOrMbN/NbNNZvYbM7sgmH+3mf2DmT1jZrvM7PNjUHfRMLMFZvbqoOk7zewrwfvGoBe60cyeM7OFY1dpQTwPfDB4/3vAFuComU02s3LgXOBlM/svZvZS8N2658TKZvbfzGyHmf0SWFTw6otP1Mz+3sy2mtk6M6sACP7tNQXvp5rZ7jGtsoiY2R+Z2VozexrYYGYNZrYl1+0US4A/BNxsZnHgAuCFQZ/dA7zi7hcAfwn830GfvQ+4BrgIuMvMxhWo3rBZDfyJuy8Dvgz87RjXk1dBb2fAzOaQ7m3/mvR36oNAE7AZWAE0kv7uLAWWmdnlZrYMuDmY9/vA8oL/BYpPI3Cfu/8e0Al8eozrKTYVg4ZPHh80/0LgBnf/cL4aLoYhFNx9U/Cr2i2ke+ODXUrwhXH3p4OxuhO/Dv/E3fuAPjM7CNQB+wpTdTiYWTXwAeAx+91TeIri555nz5MO7w8B/weYGbzvIj3E8tHgzyvB8lWkg2oC8Li79wCY2drCll2UfuvuJ3672wg0jGEtxehMQyjr3f2dfDZcTP+Q1wJfJ90zmpLhOn2D3icprr9PoQ3w7t+o4sE8AzrONkb3HvUr0oF9PukhlLeAPweOAD8EPgz8b3f//uCVBh/8lJNO/XdWEbwf/J2LF7SicOjOdwPFMoQC8A/APe6++ZT5vwA+A2BmK0iH0ZEC1xYG+4H6YJw3TnBMwd0PA21m9ikAM4uY2ZIxrLNQngc+Abzj7smgJ1RNehjleeBnwK1mVgVgZjPNbBrwHHC9mVWY2QTgD8am/FDYDSwL3uf8AJ0Mr2gC3N33ufu3h/jobtLjk5uArwErh9uWmf3UzOpzXGJRc/de4K+BZmAdMPjUr5uBz5rZa8BW0sGGmX3KzP57oWstkM2kzz75zSnzuty9w93XAQ8AvzazzcCjwAR3fxl4GHgN+H/ASydWNrPPmtlnC/UXCIGvA7eb2Suk9zVw8pS5U4dCJZDL/aNL6UVEQqpoeuAiIjIyCnARkZBSgIuIhJQCXEQkpBTgIiIhpQAXEQkpBbiISEj9f9NyG7W6DO/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  #更明显的气泡\n",
    "ax.scatter(df['x'],df['y'],s=(df['z']**2)/9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T09:47:14.053668Z",
     "start_time": "2020-03-26T09:47:13.894097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#矩阵图\n",
    "import matplotlib.lines as lines\n",
    "fig, ax= plt.subplots()\n",
    "ax.plot(df['z'],df['y'],'o')\n",
    "ax.add_artist(lines.Line2D([70,70], [30,100],color='#000000',lw=3)) #是[x1,x2],[y1,y2] 不是[x1,y1],[x2,y2]\n",
    "ax.add_artist(lines.Line2D([30,100], [65,65],color='#000000',lw=3))\n",
    "ax.set_xlim(30,100)\n",
    "ax.set_ylim(30,100)\n",
    "ax.set_xlabel(\"z\")\n",
    "ax.set_ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T08:18:08.275319Z",
     "start_time": "2020-03-25T08:18:08.154975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#给每个点都赋颜色\n",
    "fig, ax = plt.subplots() \n",
    "ax.scatter(df['x'],df['y'], s=df['z'],c=['#1EAFAE', '#A3FFFF', '#69FFFF', '#BA5C25', '#FFA069'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T09:56:27.236431Z",
     "start_time": "2020-03-24T09:56:27.114782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 折线图\n",
    "fig, ax = plt.subplots() \n",
    "ax.plot(df['x'],df['y'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T08:49:07.009321Z",
     "start_time": "2020-03-25T08:49:06.871153Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot(x,y,'o') 的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:02:33.477553Z",
     "start_time": "2020-03-24T10:02:33.359832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画多条线进行对比\n",
    "fig, ax = plt.subplots() \n",
    "ax.plot(df['x'],df['y'])\n",
    "ax.plot(df['x'],df['z'])\n",
    "ax.set_ylim(0,100)\n",
    "#三种画多条折线的写法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T06:36:53.278986Z",
     "start_time": "2020-03-25T06:36:53.159294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#面积图 核心是用fill填充出一个多边形，最后一个点不需要等于第一个点\n",
    "x = list(range(1,len(df)+1)) \n",
    "xw=x.copy()\n",
    "y=list(df['y'])\n",
    "x0=x[0]\n",
    "x1=x[-1]\n",
    "x.insert(0,x0)\n",
    "y.insert(0,0)\n",
    "x.append(x1)\n",
    "y.append(0)\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "\n",
    "ax.plot(xw,df['y']) #折线\n",
    "ax.fill(x,y,alpha=0.3)\n",
    "ax.set_xticks(x[1:-1])\n",
    "ax.set_xticklabels(df['x'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T08:07:50.317571Z",
     "start_time": "2020-03-25T08:07:50.313581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8f74c3e898>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NshTnyLfSS1KjRmIFLklaPAMuSY0y4JLUKAMuSY0y4JLUKAMuSY0y4JLUqP8HR+kmR1mFWLUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#阶梯线图\n",
    "fig,ax=plt.subplots()\n",
    "ax.step(df['x'], df['z'], where='mid', label='mid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T08:53:51.988027Z",
     "start_time": "2020-03-25T08:53:51.870342Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T09:41:03.565762Z",
     "start_time": "2020-03-26T09:41:03.442101Z"
    }
   },
   "source": [
    "### 柱状图\n",
    "- 基础柱状图\n",
    "- 等值堆叠柱状图\n",
    "- 比例堆叠柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:13:54.211072Z",
     "start_time": "2020-03-24T10:13:54.045532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#柱状图\n",
    "fig, ax = plt.subplots() \n",
    "ax.bar(df['x'],df['y'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T07:22:48.120844Z",
     "start_time": "2020-03-26T07:22:47.983182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#给柱状图标上标签\n",
    "fig,ax= plt.subplots()\n",
    "rects=ax.bar(df['x'],df['y'])\n",
    "ax.set_ylim(0,100)\n",
    "for rect in rects:\n",
    "    height = rect.get_height()\n",
    "    ax.annotate('{}'.format(height),\n",
    "            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "            xytext=(0,1),  #往上多一个像素偏移\n",
    "            textcoords=\"offset points\",\n",
    "            ha='center', va='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:18:52.783055Z",
     "start_time": "2020-03-24T10:18:52.623481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21a56fc7128>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#簇状柱 ClusterBar\n",
    "x = list(range(1,len(df)+1)) \n",
    "\n",
    "width = 0.2  #每个柱的宽度\n",
    "x1=[i-width for i in x]\n",
    "x2=[i+width for i in x]\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "rects1 = ax.bar(x1,df['y'], width*2, label='Men',color='#1EAFAE')\n",
    "rects2 = ax.bar(x2,df['z'], width*2, label='Women',color='#69FFFF')\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df['x'])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T09:07:54.150269Z",
     "start_time": "2020-03-25T09:07:54.022577Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'Mon.'),\n",
       " Text(0,0,'Tue.'),\n",
       " Text(0,0,'Wed.'),\n",
       " Text(0,0,'Thu.'),\n",
       " Text(0,0,'Fri.')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J/hW4mVEUSbIpye9Pe9YVuInRs1s+tce6r1fVfVX1EeBvgGuT3ARcBjymqj4DvBe4AXg/8M+7H5zk3CTnTusAZuAC4FeTXMPo7w747lP6Or3Nx8PaX4/Xl/5LUhNr5gxdkroz6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJauJ/Aed4y5nU8JoHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#簇状柱 ClusterBar\n",
    "x = list(range(1,len(df)+1)) \n",
    "\n",
    "width = 0.2  #每个柱的宽度\n",
    "x1=[i-width for i in x]\n",
    "x2=[i+width for i in x]\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax.bar(x1,df['y'], width*2)\n",
    "ax.bar(x2,df['z'], width*2)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df['x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:42:07.519021Z",
     "start_time": "2020-03-24T10:42:07.360446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21a57068a58>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#堆叠柱\n",
    "fig,ax= plt.subplots()\n",
    "ax.bar(df['x'],df['y'],label='Men')\n",
    "ax.bar(df['x'],df['z'],bottom=df['y'],label='Women')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T08:00:00.430388Z",
     "start_time": "2020-03-26T08:00:00.254857Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.8,0.9,'%')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#等比例堆叠柱\n",
    "fig,ax= plt.subplots()\n",
    "df['y1']=df.apply(lambda x:(x['y'])*100/(x['y']+x['z']),axis=1)\n",
    "df['z1']=df.apply(lambda x:(x['z'])*100/(x['y']+x['z']),axis=1)\n",
    "rs1=ax.bar(df['x'],df['y1'])\n",
    "rs2=ax.bar(df['x'],df['z1'],bottom=df['y1'])\n",
    "for i in range(len(rs1)):\n",
    "    h1=rs1[i].get_height()\n",
    "    ax.annotate('{0:.1f}%'.format(h1),\n",
    "            xy=(rs1[i].get_x() + rs1[i].get_width() / 2,h1//2),\n",
    "            ha='center', va='bottom')\n",
    "    ax.annotate('{0:.1f}%'.format(rs2[i].get_height()),\n",
    "            xy=(rs1[i].get_x() + rs1[i].get_width() / 2,h1+(rs2[i].get_height())//2),\n",
    "            ha='center', va='bottom')\n",
    "\n",
    "ax.set_ylim(0,100)\n",
    "ax.set_ylabel(\"%\",x=0.8,y=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:01:53.071833Z",
     "start_time": "2020-03-26T11:01:52.766659Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c8a3a7e48>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#瀑布图\n",
    "\n",
    "x=[17,-3,7,6] #原始数据\n",
    "\n",
    "def waterfall_chart(x):\n",
    "    j=0 #sum(x) 最终柱的结果\n",
    "    k=0 #k=x[i-1]\n",
    "    x0=[]\n",
    "    for i in x:\n",
    "        if i<0:\n",
    "            x0.append(j+i)\n",
    "        else:\n",
    "            x0.append(j)\n",
    "        j+=i\n",
    "\n",
    "    x1=[abs(i) for i in x]\n",
    "\n",
    "    c1=['#1EAFAE' if i>0 else '#69FFFF' for i in x]\n",
    "    c1.append('#BA5C25')\n",
    "    x0[0]=0\n",
    "    x0.append(0)\n",
    "    x1.append(j)\n",
    "    x.append(j)\n",
    "    w=list(range(1,len(x)+1))\n",
    "    fig,ax= plt.subplots(figsize=(6,5))\n",
    "    ax.bar(w,x0,alpha=0) #都透明度为0了，颜色不重要\n",
    "    rects=ax.bar(w,x1,bottom=x0,color=c1) #颜色可传一个数组的\n",
    "    ax.set_ylim(0, 30)\n",
    "    i=0\n",
    "    for rect in rects: #加上适当的文本标签 \n",
    "        height = rect.get_height()+x0[i]\n",
    "        ax.annotate('{}'.format(x[i]),\n",
    "                xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                xytext=(0,1),  # 1 points vertical offset\n",
    "                textcoords=\"offset points\",\n",
    "                ha='center', va='bottom')\n",
    "        i+=1\n",
    "    ax.set_xticklabels(['','Q1','Q2','Q3','Q4','Ys'])\n",
    "    return ax\n",
    "waterfall_chart(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T07:52:55.609356Z",
     "start_time": "2020-03-26T07:52:55.604375Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.14'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'{0:.2f}'.format(3.1415)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T08:33:58.232766Z",
     "start_time": "2020-03-26T08:33:58.128021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x29c88dcc6a0>,\n",
       "  <matplotlib.axis.YTick at 0x29c88dc6f60>,\n",
       "  <matplotlib.axis.YTick at 0x29c88db8dd8>],\n",
       " <a list of 3 Text yticklabel objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图\n",
    "fig, ax = plt.subplots() \n",
    "ax.hist(df['y'],bins=3)\n",
    "plt.yticks([0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T09:45:54.092610Z",
     "start_time": "2020-03-25T09:45:53.944009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 1., 1., 1.]),\n",
       " array([37.  , 50.25, 63.5 , 76.75, 90.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots() \n",
    "ax.hist(df['y'],bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:27:39.210433Z",
     "start_time": "2020-03-24T10:27:39.099729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21a58219cf8>,\n",
       "  <matplotlib.lines.Line2D at 0x21a58219dd8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21a58221588>,\n",
       "  <matplotlib.lines.Line2D at 0x21a582219b0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21a58219780>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21a58221dd8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21a58221eb8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图\n",
    "fig, ax = plt.subplots() \n",
    "ax.boxplot(df['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:15:41.635722Z",
     "start_time": "2020-03-24T10:15:41.518005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#条形图\n",
    "fig, ax = plt.subplots() \n",
    "ax.barh(df['x'],df['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T08:41:56.544643Z",
     "start_time": "2020-03-26T08:41:56.482782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29c88e794a8>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e799b0>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e79f28>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e81518>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e81ac8>],\n",
       " [Text(0.795183,0.760055,'76'),\n",
       "  Text(-0.352494,1.04199,'37'),\n",
       "  Text(-1.0995,-0.0331172,'90'),\n",
       "  Text(-0.0386347,-1.09932,'60'),\n",
       "  Text(0.964363,-0.529155,'50')])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#饼图\n",
    "fig, ax = plt.subplots(subplot_kw=dict(aspect=\"equal\")) \n",
    "ax.pie(df['y'],labels=df['y']) #为了得到不扁的饼，设置xy轴比例尺相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T08:40:23.142984Z",
     "start_time": "2020-03-26T08:40:23.089127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29c88e324e0>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e329b0>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e32eb8>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e3e400>,\n",
       "  <matplotlib.patches.Wedge at 0x29c88e3e940>],\n",
       " [Text(0.795183,0.760055,''),\n",
       "  Text(-0.352494,1.04199,''),\n",
       "  Text(-1.0995,-0.0331172,''),\n",
       "  Text(-0.0386347,-1.09932,''),\n",
       "  Text(0.964363,-0.529155,'')])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots() \n",
    "axes.pie(df['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T08:54:02.297140Z",
     "start_time": "2020-03-26T08:54:02.236308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29c8909f7f0>,\n",
       "  <matplotlib.patches.Wedge at 0x29c8909fcf8>,\n",
       "  <matplotlib.patches.Wedge at 0x29c890aa278>,\n",
       "  <matplotlib.patches.Wedge at 0x29c890aa7b8>,\n",
       "  <matplotlib.patches.Wedge at 0x29c890aacc0>],\n",
       " [Text(0.313719,0.703193,''),\n",
       "  Text(0.753113,0.160377,''),\n",
       "  Text(0.470765,-0.609328,''),\n",
       "  Text(-0.584519,-0.501236,''),\n",
       "  Text(-0.484464,0.598494,'')])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#环状图 及旭日图\n",
    "fig, ax = plt.subplots(subplot_kw=dict(aspect=\"equal\")) \n",
    "ax.pie(df['y'], radius=1, colors=['#1EAFAE', '#A3FFFF', '#69FFFF', '#BA5C25', '#FFA069'],startangle=90,counterclock=False,\n",
    "       wedgeprops=dict(width=0.3, edgecolor='w'))\n",
    "\n",
    "ax.pie(df['z'], radius=1-0.3, colors=['#1EAFAE', '#A3FFFF', '#69FFFF', '#BA5C25', '#FFA069'],startangle=90,counterclock=False,\n",
    "       wedgeprops=dict(width=0.3, edgecolor='w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图表元素调校\n",
    "\n",
    "[官网文档](https://matplotlib.org/tutorials/introductory/usage.html)\n",
    "\n",
    "封装为一个函数来用，画瀑布图，设置柱图的文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:05:54.416084Z",
     "start_time": "2020-03-26T11:05:54.287428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots() #给散点图加上文本标签\n",
    "w=ax.plot(df['x'],df['y'],'o') #散点图\n",
    "for i,c in df.iterrows():\n",
    "    ax.text(i,c['y']+4,c['y'],ha='center')\n",
    "    #ax.text(i-0.08,c['y']+4,c['y'])\n",
    "ax.set_ylim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:45:56.805478Z",
     "start_time": "2020-03-24T10:45:56.801488Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T10:54:09.920990Z",
     "start_time": "2020-03-24T10:54:09.486123Z"
    }
   },
   "outputs": [],
   "source": [
    "#因为可以传颜色数组，只用一个辅助列的写法\n",
    "x=[17,-3,7,6] #原始数据\n",
    "\n",
    "j=0 #sum(x[0:i-1])\n",
    "k=0 #k=x[i-1]\n",
    "x0=[]\n",
    "for i in x:\n",
    "    if i<0:\n",
    "        x0.append(j+i)\n",
    "    else:\n",
    "        x0.append(j)\n",
    "    \n",
    "    j+=i\n",
    "\n",
    "x1=[abs(i) for i in x]\n",
    "\n",
    "c1=['#1EAFAE' if i>0 else '#69FFFF' for i in x]\n",
    "c1.append('#BA5C25')\n",
    "x0[0]=0\n",
    "x0.append(0)\n",
    "x1.append(j)\n",
    "x.append(j)\n",
    "w=list(range(1,len(x)+1))\n",
    "\n",
    "fig,ax= plt.subplots(figsize=(6,5))\n",
    "ax.bar(w,x0,alpha=0) #都透明度为0了，颜色不重要\n",
    "rects=ax.bar(w,x1,bottom=x0,color=c1) #颜色是可以传一个数组的\n",
    "ax.set_ylim(0, 30)\n",
    "#后续：加上适当的文本标签 \n",
    "i=0\n",
    "for rect in rects:\n",
    "    height = rect.get_height()+x0[i]\n",
    "    ax.annotate('{}'.format(x[i]),\n",
    "            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "            xytext=(0,1),  # 1 points vertical offset\n",
    "            textcoords=\"offset points\",\n",
    "            ha='center', va='bottom')\n",
    "    i+=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 组合图绘制\n",
    "\n",
    "组合图:多种图表类型在同一张图里，突出某些信息；多是双坐标轴；\n",
    "\n",
    "单坐标轴：散点+折线； 柱状图+折线图，棒棒糖图 ，棒棒糖图，改图层顺序，zorder参数\n",
    "\n",
    "帕累托图：双坐标轴的可视化典例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T11:04:40.186534Z",
     "start_time": "2020-03-24T11:04:40.055866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21a5a8cb8d0>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots()\n",
    "ax.plot(df['x'],df['y'],'o-',markersize=10,lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T11:01:33.900410Z",
     "start_time": "2020-03-24T11:01:33.750836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21a59697eb8>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H7y8sEA0EuKWvj+PxOPH6+orWshUBzIU7br9PTvrJFUPcWrsI/Js/EGPMncAPrLWjF3+OMeYUcApgcHCwJIWKVEI6n2e6sKeJrfBk5Wo2yyNjY4wkEkwkk3RFo3xw3z5+pa+PRo+2CBYX4LT6tB2wGmzrfY4xZh/wR8BNr/T/rbWngdMAQ0NDGi8Xz0vmckwmkyy40GkyurrKmUSCR8fHWcvleFVLCx/cv583dHQQ9GCLYHHJe2thybsfFuBUsy2HeGGM/PPAB621C6UvSaRyljMZplIplivcaWKt5Xvz8ww7Dv80M0PQGI52d3NiYICDnmwRrO1NprxsO3fiHwcGgb8tvHX6c2vt10palUgZ5a1lPp1mJp0imavsZGU6l+NrhRbBl1ZWaA6Hec/u3dzW30+HB1sEG0Lrd9y1sq2rH206xK21xwq//jHwx+UqSKRckrkcs4VtYCu9snI2leLs6CgPjI6ykMmwp6GBjx06xFu7u4l6rEWwLhiitTBBWa0HKVQT9f5IVbPWspjJMJtOV3zIBODFQovguclJctbyuo4OTsbjvKa11VOTgNFAkNZIhNZIhIiC21cU4lKVMvk8s+k0c6kUmUq3CObzPF3YRfC5hQXqgkFu7e/n+MAA/R5qEYwGghd6ub2+YEguTSEuVWW5cNftRpfJcibDw+Pj3Oc4TKZS9MRi/Nb+/dzc1+eZBS/F1ZOt4TB1HqlJdkZ/iuJ7ucKqytmUO/t3J1ZXGXEcvjI+TjKf5+qWFn77wAFe39npiS4Og6ElHKYtEvFsv7lsn0JcXLPTI6ySuRwzhYnKSi/Msdbynbk5hh2Hb87OEjKGt3Z3cyIeZ39TU0VruZSGUIi2SISWcES93FVMIS6u2O4RVtZaFjIZZlIpV448S+VyPDYxwYjj8LPVVVrDYf5doUWwzQMtgpHCBGWbJihrhkJcXLHVI6zS+TyzqRSz6TQ5F07NmU6lOJtI8ODoKIvZLPsaG/mDw4d5S3e36214xT1L2rTZVE3Sn7i4YrNHWC1mMsymUq7s3Q3ww8VFRhyHJ6emyFvL9Z2dnIzHubqlxfUWwcbQ+jh3s5a+1zSFuLjickdYZfP59YnKdNqV7V9z+TxfL+wi+MLiInXBIO8cGOD4wAC9dXUVr2cjDZfIxRTi4opLHWH1ql+9kxcWFys+UQmwlMnw0NgY9ycSTKVS9MVinDpwgJt6e109zEDDJXI5+onwiJ12avjNy46wmp6mrr2D/Xe8i5ah6yse4D9fWeFMIsFXxsdJ5fO8prWVew4eZKijw9UWQQ2XyGYoxD1gu50afracydB43et506uvdeWu21rLt+bmGHEcnp2dJWwMx3p6OBGPs7exseL1FEU3DJe4PWEq/qAQ94Ctdmr4VSqXYz6dZj6TcWWsG9Z7y4stgj9fXaUtEuE39uzh1v5+WiIRV2oKYC4Ed62cQSmlo58YD9hsp4YfFbd9nUunXenrLppKJrk/keDBsTGWs1kONDbyh4cPc9S1FsH1/bmLwyVud7qIfynEPeBynRp+VdzDZDGTcWW4pOiFhQWGHYenpta/v2/s6uJkPM6R5mZXgjNsArRHoxoukZJRiHvApTo1jt51t4tVbV1xuGQuna74zoEbZfN5npqaYsRx+OHSEg3BIHfs2sXt/f30uNQi2BgK0xGN0qy9S6TEFOIe8LJODZ91p+SsZcEDwyWwvjDowdFR7k8kmEmn6a+r456DB3l7T48rO/YFTYC2SIT2SMRzBz9I9VCIe8SRozf6IrSLljIZ5jwwXALws5UVRhyHxyYmSOXzXNPWxkcOHeK17e2utObVBUN0RLXxlFSGQlw23aOeLHaXuDxcAusTps/OzjLiOHx7bo5IIMCNPT0cHxhgjwstgqbQYdIRiWifbqko/bTVuCv1qOc2dJesuTxcArCWzfLVQotgYm2N9kiEu/fu5Za+PldaBKOB4IWJSi/sHS61RyFe4y7Vo/7E5++l4brXe2K4BGAymeS+RIKHxsZYyWY52NTEHx05wg1dXS50eRiawyHaI1GaNFEpLlOI17hL9aIvz0yxkElXuJqXs9by/OIiw47DNwotgjd0dXEiHuewCy2CIROgPRKhLRrV5lPiGQrxGnepHvVYe4cL1azL5PM8WWgR/NHSEg2hEL+6axe3DwzQHYtVvJ6GUIiOSFSLcsSTNhXixpge4EvW2qPGmDDwf4F24FPW2k+Xs0ApvWw+z0Imw3w6ze4Tv85zn/00+fQv7roDkQgH7nh3xetaSKd5sLCL4Gw6Tbyujg8fPMjbensrfhp7cSl8RzSqk+DF064Y4saYNuAzQEPh0keBZ621/9UYc9YY80Vr7VI5i5SdK/ZzL2QyLGezFE+C77/+BgBe/PIXSc7OEGvv4MAd775wvRJeWl6+0CKYsZbr2tr4vUOHuNaFFsFoIEhHYaKymtoDa22XzFqymTvxHHAnMFz4/THg44WPnwCGgMc2foIx5hRwCmBwcLAUdco25K1lMZNhIZNmMfOL4L5Y//U3VDS0i7Wdn5lh2HH4zvw80UCAt/f2ciIeZ7Ch4cpfoKTWT4PviEarcr/uWtwls5Zc8SfWWrsIbBwLbAAShY9ngZ5X+JzTwGmAoaEh91sbaoi9ENwZz3SWbLSazfKV8XFGEgnG1tbojEb5zb17eUd/f8U7PYr7mLRHIoSqeKKyVnbJrFXbue1YBuqABaCx8HtxkbWW5WyW+cIKyrzHghtgfG2N+xIJHh4bYzWX41BzM+/bu5c3dXZWPEBjwSBd0RgtNTJRWc27ZMr2QvxZ4M3Al4BfBp4uaUWyacuFO+6FTMaVE+CvxFrLDxYWGHEcnp6exhjDmwstgoeamyteT2MoTGe09nq7q3GXTPmF7YT4Z4CzxpijwKuAZ0pbklzOajZ7obMk68HghvUWwScmJxlxHH68vExTKMSvDw5ye38/nRVvEVwf7+6KRmt2OXy17JIpr2zTP9XW2mOFX39qjLmZ9bvx/2KtdeeIlhpS3LNkwcUTcTZjPp3mgcIugvOZDLvq6/nIVVdxrKen4m16BkN7JEJnLFbzC3P8vEumXJmxtrzjp0NDQ/b8+fNlfY5qlMrlmM9kWEinSXk4uAF+UmgRfHxigqy1DLW3czIe55q2toqPOQdNgI5olI4qn6yU6meMedZaO3Slx9Xm+0uPSufzLKTTzGfSJHPeDu6ctXyz0CL4vUKL4C19fRyPx4nX11e8nkggSGcV9neLXIlC3GWpXK4wOen94Ib1MflHxsc54ziMJ5N0RaN8YN8+bunro9GFCcO6YIiuaNS1Q45F3KYQd0GyGNw+GCopGl9b40yhRXAtl+NVzc28f98+3tjZSdCFYYumUJiuWKwqF+eIbIX+BlRIMpe7sOzdL8FtreV78/OMOA7PzMwQMIa3dHdzYmCAgy60CIKhNbwe3trPRGSdQryM1grtgF7vKrlYOpfja5OTDDsOL62s0BwO857du7mtv5+OaLTi9QQwtEejdEajOiFe5CIK8RIr9nEveOAIs62aS6U4OzrK2dFRFjIZdjc08LFDh3hrd7crB/2Gip0m0ahOzRG5BIV4CaxksxeGSry6AOdyXlxaYsRxeGJykpy1vK6jg5PxOK9pbXVlWXp0Q6dJLSyLF9kJhfg2FPcqWSxsMuXH4M5ZyzPT0ww7Dj9YWCAWCHBrfz/vHBhgwIUWQYD6YIiuWIzmGlsWL7ITCvFNstaytCG4vbhXyWasZLM8PDbGfYkEE8kkPbEYH9q/n5t7e11pEQRoDofpisaoV6eJyJbpb81lFLd1Lf7nxd0BNyuxusp9iQSPjo+zlstxdUsLH9q/n+s7O10bb24JR+hWp4nIjijEL5K3lqVCR8mSz4PbWst35ucZdhzOz8wQLLYIxuMcaGpyrS6Ft0jpKMRZP3OyeLe9nM167iCFrUptaBH86coKreEwdxVaBNtcaBEsUniLlF7Nhnj6QnCnWcnmuNTRZX4yk0pxNpHggbExFjMZ9jY08PuHDvGW7m4iLganwlukfGoqxIvL3Rd9sk/JZv1ocZFhx+Hc1BR5a7m+s5OT8ThXt7S42qKn8BYpv6oP8ZUNHSV+WjV5Jbl8nm9MTzPiODy3uEhdMMg7BwY4PjBAb12dq7UpvEUqp+pCfGMr4JJPe7gvZzmT4aFCi+BUKkVvLMZvHzjAzb29rrfoKbxFKq8qQjxn7YXzJv3eUXIpzuoqZxyHR8fHSeXzvKa1lXsOHmSoo8P1JekKbxH3+DbEN3aULGWzVMPE5MWstXx7bo5hx+HZ2VlCxnCsp4cT8Tj7GhvdLk/hLeIBvgrxVC53IbhXc1m3yymbZC7H4xMTjDgOP1tdpS0S4b179nBrfz+tHjj8QOEt4h2eD/G1wvi2n/bh3q7pZJL7R0d5cHSUpWyW/Y2N/OHhwxzt7vbEFqwKbxHv2XKIG2PagM8B3cCz1trfKXlVBT9bWWEhky7Xl/eMHxZaBJ+cnATgDYUWwVe53CJYpPAW8a7t3Im/D/ictfZzxpj/Y4wZstaW5Tj7vK2+ce6ibD7PU1NTjCQS/HBxkYZgkJPxOO8cGKDH5RbBIoW3iPdtJ8RngKuNMa3ALuDnpS2pui1mMjw0Osp9iQQz6TT9dXXcc/Agb+/poc4ju/gpvEX8Yzup8SRwO/Ax4Hlg9uIHGGNOAacABgcHd1Jf1fjZygojjsNjExOk8nmuaWvjI4cO8dr2dgIeGDIBhbeIH20nxP8cuMdau2iM+UPgA8DpjQ+w1p4uXhsaGqreMZEryFvLt2ZnGXYcvj03RyQQWG8RHBhgjwdaBIuaQmF66+oU3iI+tJ0QbwNebYx5GrgeeLS0JflfMpfjq+PjjDgOztoa7ZEId+/dyy19fbR4oEWwqD4YoreujgaPDOOIyNZt52/vXwL/G9gNfAP4fEkr8rHJZJL7EgkeGhtjJZvlYFMTf3TkCDd0dXmiRbAoFgzSG6ujScegifjelkPcWvtPwC+VoRZfstbyQqFF8OtTUwDc0NXFiXicw83NnmgRLIoEgvTGYp56NyAiO6P30duUyed5cmqKEcfhR0tLNIRC/OquXdw+MEB3LOZ2eS8TMgF6YjGdHi9ShRTiW7SQTvPg2Bj3JxLMptPE6+r48MGDvK2313MTg0EToCsapSMa9UwHjIiUlkJ8k15aXmYkkeDxiQnS+TzXtbXxe4cOca2HWgSLDIauaJTOWMz1HQ5FpLwU4peRt5bzMzMMOw7fmZ8nGgjwtsIugoMNDW6X9woMHZEIXbGYpyZSRaR8FOKvYDWb5Svj44wkEoytrdERifCbe/dyS38/zR7t6GgNR+ipqyOi8BapKQrxDcbX1rgvkeDhsTFWczkONTfzvr17eVNnJyGPhmNzOExPTAt1RGpVzYe4tZYfLCww4jg8PT0NwJu7uzkxMMDhlhaXq7u0hlCI3lid60eyiYi7ajYBMvk8T0xOMuI4/Hh5maZQiF8fHOT2/n46PdYiuJEW6ojIRjUX4vPpNA+MjnJ/IsF8JsOu+no+ctVVHOvp8fSQRDQQpEcLdUTkIjUT4j9ZWrrQIpi1lqH2dk7G41zT1ubpBTBhE6BbC3VE5BKqOsRz1vLNQovg9wotgrf09XE8HideX+92eZelhToishlVGeKr2SyPjI1xJpFgPJmkKxrlA/v2cUtfH40eH0vWQh0R2YqqCvGxtTXOOA6PjI+zlsvxquZm3r9vH2/s7CTo0RbBX1hfqNMdi3m2nVFEvMf3IW6t5Xvz84w4Ds/MzBAwhqOFXQSvam52u7xNaQlH6InFiHp4YlVEvMm3IZ7O5fja5CTDjsNLKys0h8O8Z/dubuvvpyMadbu8TakPhuirU6+3iGyf79JjLpXi7OgoZ0dHWchk2N3QwMcOHeKt3d2+uZNVu6CIlIpvQvzFpSWGHYdzk5PkrOV1HR2cjMd5TWurb1rvQoV2wXa1C4pIiXg6xHPW8vWpKYYdhx8sLBALBLi1v593Dgww4PEWwY2KHSddsZjaBUWkpDwb4vcnEvzF977LeDJJTyzGh/bv5+beXs+3CL6cob3QcaKtYUWkHDwb4gEDPbEYH9y/n+s7O33XM63dBUWkEjwb4u/o6+dIcwtL2YzbpWxJXaHjpEEDKcPyAAAGc0lEQVQdJyJSAZ5NGr9N/OkkeRFxw7YHao0xnzTGHC9lMX4UNAH66uq5qqlJAS4iFbetO3FjzFGg11p7psT1+IbB0FnoOPHbeL2IVI8t34kbY8LA3wMvGWNOlr4k72uLRDnU3ExvXZ0CXERctZ3hlLuB54C/Al5vjPnoxQ8wxpwyxpw3xpyfmpraaY2e0RQKc7CpmXh9vVoGRcQTtpNE1wKnrbXjwGeBGy9+gLX2tLV2yFo71NXVtdMaXRcLBtnb0Miexka1DIqIp2xnTPxFYF/h4yHgp6Urx1sihT1OWjVhKSIetZ0Q/xTwaWPMXUAYeFdpS3Jf8VSdzmjUd62OIlJbthzi1tol4N1lqMUD1jtOutVxIiI+4dnFPpWmgxlExI9qPsS1TF5E/KxmkytsAvTW1WnSUkR8reZCPIChKxajS5OWIlIFaijE1/f27tFp8iJSRWoixJtCYXrrtLe3iFSfqg7xWDBIX6zOZ6cBiYhsXlWGeNgE6KmL0RaJul2KiEhZVVWImw2TljqQWERqQZWEuKGtMGmp3QVFpJb4PsQbQ2H6NGkpIjXKtyEeDQTpq6ujSZOWIlLDfBfiIROgJxajLRLRYh0RqXm+CXGDoatwpqUmLUVE1vkixFvDEXrq6oho0lJE5GU8HeL1oRA9sRh12mFQROQVeTodu2Oxij/n8+ce49wX7mVpZpqmjk6O3nU3R47+m2NERUQ8wdMhXmnPn3uMh09/gmw6BcDS9BQPn/4EgIJcRDxJg8wbnPvCvRcCvCibTnHuC/e6VJGIyOUpxDdYmpne0nUREbcpxDdo6ujc0nUREbcpxDc4etfdhC7a+TAUiXL0rrtdqkhE5PK2PbFpjOkBHrTWXlvCelxVnLxUd4qI+MVOulP+GqgrVSFeceTojQptEfGNbYW4MeZtwAowXtpy1qlXW0Rkc7Y8Jm6MiQD/Gfj4ZR5zyhhz3hhzfmpqaktfv9irvTQ9BdZe6NV+/txjWy1VRKTqbWdi8+PAJ62185d6gLX2tLV2yFo71NXVtaUvrl5tEZHN206I3wT8rjHmceAaY8w/lLIg9WqLiGzelsfErbVvKX5sjHncWvtbpSyoqaNzfSjlFa6LiMjL7ahP3Fp7rER1XKBebRGRzfPcBljq1RYR2TzPhTioV1tEZLO07F5ExMcU4iIiPqYQFxHxMYW4iIiPKcRFRHzMWGvL+wTGTAE/3eandwK1tlRTr7k26DXXhp285t3W2ivuW1L2EN8JY8x5a+2Q23VUkl5zbdBrrg2VeM0aThER8TGFuIiIj3k9xE+7XYAL9Jprg15zbSj7a/b0mLiIiFye1+/ERUTkMhTiHmGMaTHGPGCMedgY8/8Kx+DVBGNMjzHm227XUUnGmE8aY467XUclGGPajDFnC0c2/p3b9VQbz4a4MeZTxphvGGP+zO1aKuS9wN9Ya3+F9QOo3+FyPZX010Cd20VUijHmKNBrrT3jdi0V8j7gc4VWuyZjTFW3GRZuSs4VPg4bY84YY54yxnywHM/nyRA3xvwaELTWvhHYZ4w56HZN5Wat/aS19pHCb7uASTfrqRRjzNuAFdb/4ap6xpgw8PfAS8aYk27XUyEzwNXGmFZgF/Bzl+spG2NMG/AZoKFw6aPAs9baG4B3GWOaSv2cngxx4Bjwj4WPHwbe7F4plWWMeSPQZq192u1ayq0wZPSfWT98u1bcDTwH/BXwemPMR12upxKeBHYDHwOeB2bdLaescsCdwGLh98f4RZY9AZT8XYhXQ7wBSBQ+ngV6XKylYowx7cDfAmV52+VBHwc+aa2dd7uQCroWOG2tHQc+C9TC6Sd/Dtxjrf0L4AXgAy7XUzbW2kVr7cKGS2XPMq+G+DK/GCNtxLt1lkzhrvSLwJ9Ya7e714zf3AT8rjHmceAaY8w/uFxPJbwI7Ct8PMT29xXykzbg1caYIHA9UEt9zWXPMq+G47P8Ygjll4GX3CulYj4EXAf8qTHmcWPMnW4XVG7W2rdYa48VDtz+Z2vtb7ldUwV8CrjRGPME8GHWJ3Wr3V+yvuhlAWgHPu9uORVV9izz5GIfY0wzcA74CnAr8IaL3qKIiHiWMeZxa+0xY8xu4CzwKPAm1rMsV9Ln8mKIw4VZ3puBJwrjhyIivmOM6Wf9bvyhctyMejbERUTkyrw6Ji4iIpugEBcR8TGFuIiIjynERUR8TCEuIuJj/x9De3f1ioCcmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点+折线 抄自https://matplotlib.org/gallery/lines_bars_and_markers/fill_between_demo.html#sphx-glr-gallery-lines-bars-and-markers-fill-between-demo-py\n",
    "N = 21\n",
    "x = np.linspace(0, 10, 11)\n",
    "y = [3.9, 4.4, 10.8, 10.3, 11.2, 13.1, 14.1,  9.9, 13.9, 15.1, 12.5]\n",
    "\n",
    "# fit a linear curve an estimate its y-values and their error.\n",
    "a, b = np.polyfit(x, y, deg=1)\n",
    "y_est = a * x + b\n",
    "y_err = x.std() * np.sqrt(1/len(x) +\n",
    "                          (x - x.mean())**2 / np.sum((x - x.mean())**2))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y_est, '-')\n",
    "ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2)\n",
    "ax.plot(x, y, 'o', color='tab:brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:33:06.910761Z",
     "start_time": "2020-03-26T11:33:06.714285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29c8a932978>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#棒棒糖图\n",
    "y = [5, 4, 11, 10, 15, 11, 13, 8,13,15,13,19]\n",
    "fig, ax= plt.subplots(figsize=(6,6)) \n",
    "\n",
    "x=['c'+str(i) for i in range(1,len(y)+1)]\n",
    "\n",
    "ax.barh(x,y,height=0.08,zorder=1) #图层顺序的解决方案\n",
    "ax.scatter(y,x,zorder=2,color='#ba5c25') #改了zorder之后渲染颜色的选择也改了，就需要再手动设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:34:39.491808Z",
     "start_time": "2020-03-26T11:34:39.286358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#哑铃图\n",
    "y = [5, 4, 11, 10, 15, 11, 13, 8,13,15,13,19]\n",
    "z=[1,-3,1,-1,2,4,-2,3,1,2,3,2]\n",
    "fig, ax= plt.subplots(figsize=(6,6)) \n",
    "y=sorted(y) #先排个序\n",
    "\n",
    "y1=[y[i]-z[i] for i in range(len(y))]\n",
    "x=['c'+str(i) for i in range(1,len(y)+1)]\n",
    "x1=[0 for i in range(len(y))]\n",
    "z1=[min(y[i],y1[i]) for i in range(len(y))]\n",
    "z2=[max(y[i],y1[i]) for i in range(len(y))]\n",
    "ax.hlines(x,z1,z2,color='#69ffff')\n",
    "ax.scatter(y,x,zorder=2,color='#ba5c25') \n",
    "ax.scatter(y1,x,zorder=2,color='#1eafae') \n",
    "ax.legend(('delta','Male', 'Female'), loc='upper left')\n",
    "ax.set_xlim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:41:16.607799Z",
     "start_time": "2020-03-26T11:41:16.263721Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:28:09.026813Z",
     "start_time": "2020-03-26T11:28:08.789422Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Pareto in Matplotlib')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#帕累托图\n",
    "y=[23,162,51,119,12,3,8] #模拟数据\n",
    "x=[str(i) for i in range(2,len(y)+2)]\n",
    "y=sorted(y,reverse=True)\n",
    "ysum=sum(y)\n",
    "y2=[]\n",
    "cc=0\n",
    "for i in y:\n",
    "    cc+=i\n",
    "    y2.append(cc/ysum*100)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "rects=ax1.bar(x,y,color='#1EAFAE')\n",
    "ax1.set_ylabel('Month IC')\n",
    "ax1.set_ylim(0, 180)\n",
    "ax2 = ax1.twinx() #https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx\n",
    "ax2.set_ylim(0, 100)\n",
    "\n",
    "ax2.plot(x, y2,'o',color='#FFA069',linewidth=2,ls='-')\n",
    "\n",
    "ax2.set_ylabel('%')\n",
    "\n",
    "for rect in rects:\n",
    "    height = rect.get_height()\n",
    "    ax1.annotate('{}'.format(height),\n",
    "            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "            xytext=(0,1),  # 1 points vertical offset\n",
    "            textcoords=\"offset points\",\n",
    "            ha='center', va='bottom')\n",
    "ax1.set_title(\"Pareto in Matplotlib\") #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开双轴关键点：ax2 = ax1.twinx()\n",
    "\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx\n",
    "\n",
    "\n",
    "分面、子图\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T10:27:14.045344Z",
     "start_time": "2020-03-25T10:27:13.842625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.axes._subplots.AxesSubplot"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAABjCAYAAAArfcQDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAABp9JREFUeJzt3V+MXGUdxvHvAxWJDYKhJTFaLcRiXRsSysbgjWL8k1KT9kI0bUK0proBQS64IiFRg1eSoAlJI1ljA5iIRW5cTYkJCikhLrANFUoNZkWMG4ktf+SGUGn8eXFO6PS3s513d945U+D5JJOcM+ed83vP7jxz5sw5Oa8iAjM76axxd8DsTONQmCUOhVniUJglDoVZ4lCYJQNDIWmvpKOSDi+xXJLulDQv6WlJm+t306w7JXuKu4Etp1l+NbChfUwBPx2+W2bjMzAUEXEAeOU0TbYD90ZjFrhA0gdrddCsazWOKT4E/LNnfqF9zuxtaVWFdajPc32vHZE0RfMVi9WrV1+xcePGCuXNFjt48OBLEbF2Ja+tEYoFYF3P/IeBf/VrGBHTwDTA5ORkzM3NVShvtpikf6z0tTW+Ps0AX29/hboSeC0iXqywXrOxGLinkHQfcBWwRtIC8H3gPQARcRewH9gKzAOvA98cVWfNujAwFBGxc8DyAG6o1iOzMfMZbbPEoTBLHAqzxKEwSxwKs8ShMEscCrPEoTBLHAqzxKEwSxwKs8ShMEscCrPEoTBLHAqzxKEwSxwKs8ShMEscCrPEoTBLHAqzxKEwSxwKs6QoFJK2SHquHYPilj7Ld0k6JulQ+/hW/a6adaPkDoFnA3uAL9LcN/ZJSTMRcSQ13RcRN46gj2adKtlTfAqYj4jnI+K/wK9oxqQwe0cqCUXp+BNfaYf3ekDSuj7Lzd4WSkJRMv7Eb4H1EXEZ8BBwT98VSVOS5iTNHTt2bHk9NetISSgGjj8RES9HxPF29mfAFf1WFBHTETEZEZNr165oPA2zkSsJxZPABkkXSzoH2EEzJsVb0hh324C/1OuiWbdKbsV/QtKNwO+Bs4G9EfGspNuAuYiYAW6StA04QTNo5K4R9tlspNQML9E9D+9loyTpYERMruS1PqNtljgUZolDYZY4FGaJQ2GWOBRmiUNhljgUZolDYZY4FGaJQ2GWOBRmiUNhljgUZolDYZY4FGaJQ2GWOBRmiUNhljgUZolDYZY4FGaJQ2GW1Bqf4r2S9rXLH5e0vnZHzboyMBQ941NcDUwAOyVNpGa7gVcj4mPAT4Af1e6oWVdqjU+xnZN3Gn8A+LykfncrNzvj1Rqf4q02EXECeA24sEYHzbo28AbLlI1PUdIGSVPAVDt7XNLhgvqjsAZ4yXXf0bU/vtIXloRi4PgUPW0WJK0Czqe5+/gpImIamAaQNLfSG+AOa1y13211x1lb0orv3l1lfIp2/hvt9DXAH2NctzM3G1Kt8Sl+DvxC0jzNHmLHKDttNkolX5+IiP3A/vTc93qm3wC+usza08tsX9O4ar/b6o6z9orrjm3QFrMzlS/zMEtGHopxXSJSUPdmSUfasb//IOmjNeqW1O5pd42kkFTl15mSupK+1m73s5J+WaNuSW1JH5H0sKSn2r/51go190o6utRP+2rc2fbpaUmbi1YcESN70ByY/w24BDgH+DMwkdp8B7irnd4B7Ouo7ueA97XT19eoW1q7bXcecACYBSY72uYNwFPAB9r5izr8P08D17fTE8ALFep+BtgMHF5i+VbgQZrzaFcCj5esd9R7inFdIjKwbkQ8HBGvt7OzNOdfaijZZoAfArcDb3RY99vAnoh4FSAijnZYO4D3t9Pns/hc17JFxAH6nA/rsR24NxqzwAVpeOu+Rh2KcV0iUlK3126aT5QaBtaWdDmwLiJ+V6lmUV3gUuBSSY9JmpW0pcPaPwCulbRA80vmdyvVHrZfixT9JDuEapeIjKBu01C6FpgEPjtkzaLaks6iuZJ4V6V6RXVbq2i+Ql1Fs2d8VNKmiPhPB7V3AndHxB2SPk1zXmtTRPxvyNrD9muRUe8plnOJCKe7RGQEdZH0BeBWYFtEHB+yZmnt84BNwCOSXqD5rjtT4WC79G/9m4h4MyL+DjxHE5JhldTeDdwPEBF/As6luS5qlIreB4vUONA6zYHQKuB54GJOHoB9MrW5gVMPtO/vqO7lNAeHG7re5tT+EeocaJds8xbgnnZ6Dc1Xiws7qv0gsKud/kT75lSF2utZ+kD7y5x6oP1E0TprviGW6NhW4K/tG/DW9rnbaD6dofnE+DUwDzwBXNJR3YeAfwOH2sdMV9uc2lYJReE2C/gxcAR4BtjR4f95AnisDcwh4EsVat4HvAi8SbNX2A1cB1zXs7172j49U/p39hlts8RntM0Sh8IscSjMEofCLHEozBKHwixxKMwSh8Is+T9QrwqtN3qzgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plt.subplot(3,2,4)\n",
    "type(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T10:27:29.044190Z",
     "start_time": "2020-03-25T10:27:28.683873Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(3,2)\n",
    "type(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T10:48:54.297954Z",
     "start_time": "2020-03-25T10:48:54.072546Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(3,2,1)\n",
    "ax1.text(0.5,0.5,'绘图区域1',ha='center',fontsize=18)\n",
    "ax2=plt.subplot(3,2,2)\n",
    "ax2.text(0.5,0.5,'绘图区域2',ha='center',fontsize=18)\n",
    "ax3=plt.subplot(3,2,3)\n",
    "ax3.text(0.5,0.5,'绘图区域3',ha='center',fontsize=18)\n",
    "ax4=plt.subplot(3,2,4)\n",
    "ax4.text(0.5,0.5,'当前绘图区域',ha='center',color='#1EAFAE',fontsize=20)\n",
    "ax5=plt.subplot(3,2,5)\n",
    "ax5.text(0.5,0.5,'绘图区域5',ha='center',fontsize=18)\n",
    "ax6=plt.subplot(3,2,6)\n",
    "ax6.text(0.5,0.5,'绘图区域6',ha='center',fontsize=18)\n",
    "\n",
    "ax1.set_xticks([])\n",
    "ax2.set_xticks([])\n",
    "ax3.set_xticks([])\n",
    "ax5.set_xticks([])\n",
    "ax6.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax2.set_yticks([])\n",
    "ax3.set_yticks([])\n",
    "ax5.set_yticks([])\n",
    "ax6.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T04:11:51.253324Z",
     "start_time": "2020-03-26T04:11:51.047783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(3,2,1)\n",
    "\n",
    "ax2=plt.subplot(3,2,2)\n",
    "\n",
    "ax3=plt.subplot(3,2,3)\n",
    "\n",
    "ax4=plt.subplot(3,2,4)\n",
    "\n",
    "ax5=plt.subplot(3,2,5)\n",
    "\n",
    "ax6=plt.subplot(3,2,6)\n",
    "\n",
    "\n",
    "ax1.set_xticks([])\n",
    "ax2.set_xticks([])\n",
    "ax3.set_xticks([])\n",
    "ax5.set_xticks([])\n",
    "ax6.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax2.set_yticks([])\n",
    "ax3.set_yticks([])\n",
    "ax5.set_yticks([])\n",
    "ax6.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三维及科学可视化\n",
    "\n",
    "matplotlib非常擅长的领域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T11:16:25.441466Z",
     "start_time": "2020-03-25T11:16:25.367629Z"
    }
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T11:16:58.749206Z",
     "start_time": "2020-03-25T11:16:58.561780Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x1b1314b2a90>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.plot(range(len(df)),df['y'],df['z'],'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-26T11:11:36.857421Z",
     "start_time": "2020-03-26T11:11:36.670894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x29c8a5df978>,\n",
       " <matplotlib.axis.XTick at 0x29c8a575748>,\n",
       " <matplotlib.axis.XTick at 0x29c8a61ba58>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.bar(df['x'],df['y'], zs=0, zdir='y', alpha=0.8)\n",
    "ax.bar(df['x'],df['z'], zs=1, zdir='y', alpha=0.8)\n",
    "ax.bar(df['x'],df['y'], zs=2, zdir='y', alpha=0.8)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "ax.set_yticks([0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T11:24:54.803605Z",
     "start_time": "2020-03-25T11:24:54.652005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b132ece3c8>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三角函数图像\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2 * np.pi * t)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-24T11:08:29.000538Z",
     "start_time": "2020-03-24T11:08:28.815070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2 * np.pi * t)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, s)\n",
    "#极坐标下的心形线（一定要加上方程的文本标签）\n",
    "theta= np.arange(0, 2*np.pi, 0.05)\n",
    "r=5*(1-np.sin(theta))\n",
    "ax = plt.subplot(111, projection='polar')\n",
    "ax.plot(theta, r,lw=3)\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 动态可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 底图水印\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 主题色配置\n",
    "\n",
    "`mpl.style.use('lyncolss') `\n",
    "\n",
    "支持中文\n",
    "\n",
    "`mpl.rcParams['font.family']='SimHei'`\n",
    "\n",
    "图片的导出与保存\n",
    "\n",
    "`plt.savefig(fpath,dpi=300)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-25T10:34:08.686983Z",
     "start_time": "2020-03-25T10:34:08.681997Z"
    }
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['font.family']='SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 其他深入话题简介\n",
    "- **极坐标**:\n",
    "- **地图+basemap**:\n",
    "\n",
    "## 参考资料\n",
    "\n",
    "- 官网是最好的参照：https://matplotlib.org/\n",
    "- matplotlib：先搞明白plt. /ax./ fig再画 - 姚太多啊的文章 - 知乎\n",
    "  https://zhuanlan.zhihu.com/p/93423829\n",
    "- 看所有线类型支持的属性：https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D \n",
    "- 嵩天老师MOOC课程https://www.icourse163.org/course/0809BIT021B-1001870002\n",
    "- https://matplotlib.org/tutorials/introductory/usage.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
